I heard that the now supports voice. How does it manage the latency during multi-agent handoffs in a voice-first application? I'm worried that switching specialists might cause a long pause in the conversation.
3 answers
The SDK includes specific support for 'Realtime Agents' using models like gpt-realtime-1.5. It uses a WebSocket connection to handle speech-to-speech streaming, which significantly reduces latency. When a handoff occurs between agents in a voice session, the SDK is optimized to transition the context as quickly as possible. However, the perceived latency depends on how complex your tools are. If the new agent needs to call a slow API, you'll still have a gap. Using lightweight tools and ensuring your session management is efficient is key to keeping the conversation flowing naturally.
Is it possible to use the Voice SDK with external providers, or are we locked into the OpenAI platform?
The streaming capabilities in the new update make the response time feel much more like a human conversation.
I've been testing it for a customer support bot and the interruption handling is surprisingly good for an early-stage SDK.
Patrick, while the SDK is designed to be provider-agnostic for text, the specific Realtime Voice features are currently most optimized for the OpenAI provider. However, because it's built on standard WebSockets and Python, you can technically wrap other voice providers as tools or use the 'Model' interface to extend it. It might require more custom infrastructure compared to the native OpenAI integration, but it's definitely feasible with some engineering effort.